2026-01-27_lifecyle_churn_analysis

Meeting Title: Lifecyle & churn analysis Date: 2026-01-27 Meeting participants: Judd Kuehling, Zoran Selinger, Greg Stoutenburg, Mitesh Patel

WEBVTT

1 00:01:49.680 ⇒ 00:01:50.660 Zoran Selinger: Hi, John.

2 00:01:51.410 ⇒ 00:01:52.250 Judd Kuehling: Hello.

3 00:01:52.610 ⇒ 00:01:53.670 Zoran Selinger: Good morning.

4 00:01:54.110 ⇒ 00:01:54.880 Judd Kuehling: Morning.

5 00:01:55.340 ⇒ 00:01:58.219 Zoran Selinger: Is this a little bit early for you? Are you on the West Coast?

6 00:01:58.640 ⇒ 00:01:59.420 Judd Kuehling: Yes.

7 00:01:59.660 ⇒ 00:02:00.419 Judd Kuehling: Oh, yeah, yeah.

8 00:02:00.660 ⇒ 00:02:01.570 Judd Kuehling: 7th.

9 00:02:02.240 ⇒ 00:02:04.010 Zoran Selinger: Sorry about that.

10 00:02:04.010 ⇒ 00:02:04.340 Judd Kuehling: Okay.

11 00:02:21.260 ⇒ 00:02:22.170 Zoran Selinger: Dave.

12 00:02:23.710 ⇒ 00:02:25.609 Zoran Selinger: Greg will join us.

13 00:02:54.940 ⇒ 00:02:55.780 Greg Stoutenburg: Morning, guys.

14 00:02:56.950 ⇒ 00:02:57.740 Zoran Selinger: Hi, Greg.

15 00:02:58.190 ⇒ 00:02:59.080 Greg Stoutenburg: Hello.

16 00:03:01.220 ⇒ 00:03:02.690 Zoran Selinger: You two have met?

17 00:03:03.960 ⇒ 00:03:04.570 Zoran Selinger: Before…

18 00:03:04.570 ⇒ 00:03:06.719 Greg Stoutenburg: Late last week?

19 00:03:06.720 ⇒ 00:03:07.809 Zoran Selinger: Or maybe the week before.

20 00:03:08.990 ⇒ 00:03:09.810 Zoran Selinger: Good, good.

21 00:03:56.240 ⇒ 00:03:58.260 Zoran Selinger: Let’s see if Mitesh can attend.

22 00:03:58.420 ⇒ 00:04:01.090 Zoran Selinger: I would really like him to… oh, he’s here.

23 00:04:05.200 ⇒ 00:04:06.670 Mitesh Patel: How are you?

24 00:04:06.670 ⇒ 00:04:10.279 Zoran Selinger: I’ll send you a message, checking if you’re joining.

25 00:04:11.250 ⇒ 00:04:14.279 Mitesh Patel: Yeah, I think I saw it pop in just, like, when I was joining, so…

26 00:04:14.280 ⇒ 00:04:16.540 Zoran Selinger: Yeah, yeah, exactly, exactly.

27 00:04:18.620 ⇒ 00:04:19.470 Zoran Selinger: Okay.

28 00:04:20.149 ⇒ 00:04:21.439 Zoran Selinger: How are you, Nitesh?

29 00:04:21.440 ⇒ 00:04:22.830 Mitesh Patel: I’m good. How about you?

30 00:04:23.640 ⇒ 00:04:24.740 Zoran Selinger: Yeah, yeah.

31 00:04:25.020 ⇒ 00:04:27.930 Zoran Selinger: Good. Week’s going well.

32 00:04:28.610 ⇒ 00:04:28.990 Mitesh Patel: Yeah, good.

33 00:04:28.990 ⇒ 00:04:34.039 Zoran Selinger: really productive here. Sent you a few notes there earlier.

34 00:04:34.720 ⇒ 00:04:37.199 Zoran Selinger: Okay, let me share my screen.

35 00:04:38.390 ⇒ 00:04:43.790 Zoran Selinger: And yeah, we’ll jump in. So, basically, today’s presentation, so,

36 00:04:44.160 ⇒ 00:04:55.010 Zoran Selinger: we’re kind of still trying to make sense of exactly what’s, what’s being done in customer I.O. So I, I was thinking about this,

37 00:04:55.680 ⇒ 00:04:58.640 Zoran Selinger: And this presentation is, like, a good…

38 00:04:58.810 ⇒ 00:05:12.470 Zoran Selinger: kind of jumping board to get into more… kind of more in-depth conversation about specific things, and now we have, unfortunately, Greg did not join early enough for… to contribute much in this.

39 00:05:13.190 ⇒ 00:05:32.259 Zoran Selinger: in these slides, but it’s his specialty, so I think Greg will be able to, will be able to contribute a lot in the following weeks, on this particular… So, basically, I, I want to have, I can open a few conversations about, particular things.

40 00:05:32.630 ⇒ 00:05:33.060 Mitesh Patel: Okay.

41 00:05:33.770 ⇒ 00:05:41.369 Zoran Selinger: In lifestyle marketing, one of which, and Mitesh, you know this already, we attempted to have this conversation, is

42 00:05:41.710 ⇒ 00:06:01.609 Zoran Selinger: you know, how do we measure specific things like churn and reactivations based on what? So, we’re gonna… I have those slides again here, and there’s just… essentially just a place for… you saw them, you saw them already, so just at that point, I would really like us to discuss and,

43 00:06:01.610 ⇒ 00:06:07.630 Zoran Selinger: put some definitions in place. But before that, let me share my screen, and let me go into…

44 00:06:10.740 ⇒ 00:06:12.099 Zoran Selinger: Let me see…

45 00:06:16.360 ⇒ 00:06:20.209 Zoran Selinger: Let me just put you here… Okay, so…

46 00:06:20.540 ⇒ 00:06:39.869 Zoran Selinger: majority of this data, so I have three, three sources for this. First, portion, so channel performance, product performance, and campaign, strategy, performance, this is all coming from just campaign performance in customer I.O.

47 00:06:39.870 ⇒ 00:06:55.650 Zoran Selinger: Okay, this is what I could… the groups that I could put here, are essentially either, either, like, defined the channel in customer I.O, or what I could gather from the campaign name, right? So.

48 00:06:55.870 ⇒ 00:07:00.839 Zoran Selinger: Like, products being directly in the campaign name, or campaign type, like…

49 00:07:01.270 ⇒ 00:07:07.059 Zoran Selinger: cart abandon as being in the campaign here. So I was attempting to group them as much as I can.

50 00:07:07.220 ⇒ 00:07:19.670 Zoran Selinger: and kind of group metrics based on it as much as I can. So it’s not going to be 100% accurate, and maybe some groups will overlap.

51 00:07:20.930 ⇒ 00:07:25.600 Zoran Selinger: Especially in the campaign strategy, performance.

52 00:07:25.740 ⇒ 00:07:30.010 Zoran Selinger: Campaign types, so maybe there’ll be some overlap, but…

53 00:07:30.520 ⇒ 00:07:35.880 Zoran Selinger: it’s a good place for us to discuss and define a few things.

54 00:07:36.120 ⇒ 00:07:45.430 Zoran Selinger: So, in channels, essentially, we have a few of them. So the data is just last 12 months.

55 00:07:47.270 ⇒ 00:08:06.959 Zoran Selinger: And we can see… so I was just… just looking how… how are we looking at just the number of… of sent messages, and we do have a few of webhooks, a few Slack messages that are recorded in there, but really, almost everything is email, and we do have,

56 00:08:08.220 ⇒ 00:08:22.889 Zoran Selinger: SMS via, via Twilio. So it’s… in the platform, Greg, you, you might have noticed, it’s, it’s Twilio in, in the platform. So this was… I just renamed it for, for, for a graph.

57 00:08:23.180 ⇒ 00:08:40.080 Zoran Selinger: To be SMS. So yeah, we’re, we’re mostly, sending, sending emails, and, and Judd, you’re well aware of the, of the, of this, and kind of how it compares in terms of numbers to, to one another. So we have,

58 00:08:40.100 ⇒ 00:08:43.940 Zoran Selinger: Over 11 million messages sent in a year.

59 00:08:44.110 ⇒ 00:08:48.590 Zoran Selinger: Here. Judd, does that, that make sense to you?

60 00:08:49.660 ⇒ 00:08:56.329 Judd Kuehling: Yeah, yeah, I haven’t looked at the exact numbers over the year, but that makes sense. It sounds… right.

61 00:08:56.330 ⇒ 00:09:02.549 Zoran Selinger: If you see anything that’s off to you, let me know, and we’ll check again, we’ll follow up on…

62 00:09:02.670 ⇒ 00:09:04.440 Zoran Selinger: On, on those slides.

63 00:09:04.690 ⇒ 00:09:14.809 Judd Kuehling: So, yeah, so historically, SMS was used for transactional messaging up until I started, and so the volume before me was…

64 00:09:15.020 ⇒ 00:09:20.689 Judd Kuehling: Instead of being 10% of the total, it was probably 2% of the total.

65 00:09:22.350 ⇒ 00:09:29.550 Judd Kuehling: Yeah, that started to grow since in this last quarter of last year, but yeah, it’s still a smaller.

66 00:09:30.520 ⇒ 00:09:43.099 Zoran Selinger: Yeah, we’ll look into that, because we do see some significant changes in some of the rates, like the click rates and conversion rates of

67 00:09:43.110 ⇒ 00:09:58.659 Zoran Selinger: Of, of, SMS. So, the open rates, we don’t have… we don’t really have it for… for SMS. I don’t think that those are… those are correctly, collected, but the… the open rate for emails are really good, 45%.

68 00:09:58.680 ⇒ 00:10:01.680 Zoran Selinger: percent open rate is really good, so…

69 00:10:01.910 ⇒ 00:10:14.050 Zoran Selinger: whatever we are doing in subject lines and in the first sentences of the text, seems to be good. Greg, you want to say something?

70 00:10:14.900 ⇒ 00:10:18.620 Greg Stoutenburg: I was trying to get to the mute button before you had to hear me sneeze, and I.

71 00:10:18.620 ⇒ 00:10:30.180 Zoran Selinger: Oh, okay, okay. Okay, so that is… that is really strong. Obviously, a lot of these, some of these messages will be, like, our…

72 00:10:30.180 ⇒ 00:10:43.230 Zoran Selinger: welcome flow, so those will typically have really high open rates, they have to be. But still, still the total of, of, you know, 11 million messages is… that’s, that, that is a pretty good open rate. Yeah.

73 00:10:43.870 ⇒ 00:10:46.959 Zoran Selinger: Click rates are also really strong.

74 00:10:47.150 ⇒ 00:10:56.760 Zoran Selinger: So, you know, over 2.5% for, for both SMS and email. Also, really, really strong.

75 00:10:56.980 ⇒ 00:10:59.959 Zoran Selinger: It’s, it’s really good to see.

76 00:11:00.160 ⇒ 00:11:04.760 Zoran Selinger: These are, these are pretty, pretty effective, email campaigns.

77 00:11:05.000 ⇒ 00:11:09.610 Judd Kuehling: So my data for click rates on SMS is closer to 10%.

78 00:11:10.500 ⇒ 00:11:13.670 Judd Kuehling: again, just probably looking at August forward.

79 00:11:13.890 ⇒ 00:11:20.660 Judd Kuehling: Click rates for SMS is always going to be better than email, so I’d be curious to look at kind of where the data…

80 00:11:21.400 ⇒ 00:11:27.220 Zoran Selinger: Let me… let me just… I’ll write that down to look into that. I think…

81 00:11:27.500 ⇒ 00:11:37.680 Zoran Selinger: the breakdown of the channel here in Customer I.O, that’s pretty clear. It just, It seems… I…

82 00:11:39.050 ⇒ 00:11:40.859 Judd Kuehling: You’re looking at total clicks…

83 00:11:41.120 ⇒ 00:11:42.300 Zoran Selinger: Yeah, it shouldn’t…

84 00:11:43.670 ⇒ 00:11:47.209 Judd Kuehling: And then the click rate you have is 2 point something. That’s interesting. Okay.

85 00:11:47.750 ⇒ 00:11:49.809 Judd Kuehling: Hopefully, should be…

86 00:11:49.810 ⇒ 00:11:53.139 Zoran Selinger: 2.7, they’re very similar for both channels.

87 00:11:53.400 ⇒ 00:11:57.870 Judd Kuehling: That doesn’t sound right for SMS, the click rate should be… 10%.

88 00:11:59.030 ⇒ 00:12:02.240 Judd Kuehling: So, yeah, I’d be curious to look at the data on that.

89 00:12:02.650 ⇒ 00:12:05.399 Greg Stoutenburg: Judd, are you looking at customer I.O. as well?

90 00:12:05.710 ⇒ 00:12:09.229 Judd Kuehling: Yeah, yeah, I’m there every hour of the day, yeah.

91 00:12:09.400 ⇒ 00:12:14.129 Greg Stoutenburg: Yeah, sorry, I mean, the 10% number that you have offered, that’s from customer out, okay.

92 00:12:14.130 ⇒ 00:12:14.540 Judd Kuehling: Right.

93 00:12:14.600 ⇒ 00:12:22.509 Zoran Selinger: Okay, so it’s a… it’s a big difference. Let me check. I’ll follow up on… on that particular one.

94 00:12:23.560 ⇒ 00:12:35.070 Zoran Selinger: So we… we were looking at the… the conversion… I mean, we were looking at every metric, so we have 10 metrics here. I only put things that are kind of really interesting. I have…

95 00:12:35.760 ⇒ 00:12:38.959 Zoran Selinger: Metric breakdowns for every category.

96 00:12:39.690 ⇒ 00:12:43.070 Zoran Selinger: for everything, basically. I have a hundred…

97 00:12:43.210 ⇒ 00:12:54.930 Zoran Selinger: a hundred, graphs here for this analysis. We only put, like, basically the highlights and things that are, that are similar, a little bit more interesting.

98 00:12:55.250 ⇒ 00:12:57.629 Zoran Selinger: So you see the conversion rate?

99 00:12:57.980 ⇒ 00:13:01.230 Zoran Selinger: Sorry, the conversion volume and conversion rates.

100 00:13:01.410 ⇒ 00:13:10.270 Zoran Selinger: They’re… really different. You see, the change is really significant.

101 00:13:10.460 ⇒ 00:13:19.080 Zoran Selinger: or after, basically, after July. I mean, it’s been… it’s been decreasing throughout the whole year.

102 00:13:19.200 ⇒ 00:13:37.140 Zoran Selinger: steadily going down, especially the SMS, and you can see that both charts, both for volume and for rates, is going down. And then, ever since, it’s been fairly stable, with September looking a little bit better.

103 00:13:37.350 ⇒ 00:13:43.179 Zoran Selinger: So, we wanted to understand what exactly happened here. Do you have an idea already?

104 00:13:43.840 ⇒ 00:13:52.809 Judd Kuehling: Yeah, so I mean, we didn’t… conversion rate for SMS, that’s transactional SMS, so looking at conversion rate isn’t really…

105 00:13:52.950 ⇒ 00:14:12.510 Judd Kuehling: useful, like, there’s not really any conversion. It was like, your order has been delivered, like, there’s not a conversion we’re seeking there. I don’t know what the measurement was on some of those emails, and in fact, like, there probably shouldn’t even be a goal to conversion on some of those emails.

106 00:14:12.510 ⇒ 00:14:12.840 Zoran Selinger: Yeah.

107 00:14:12.840 ⇒ 00:14:26.660 Judd Kuehling: It’s purely transactional. So there are some emails and texts that were set up before I came that have conversion rate goals that aren’t really relevant to being actionable.

108 00:14:26.960 ⇒ 00:14:28.750 Judd Kuehling: So.

109 00:14:28.750 ⇒ 00:14:29.150 Zoran Selinger: Okay.

110 00:14:29.150 ⇒ 00:14:31.580 Judd Kuehling: It’s like it’s a… it’s a delivery message.

111 00:14:32.000 ⇒ 00:14:35.810 Judd Kuehling: It doesn’t have a call to action or anything like that, and then…

112 00:14:35.810 ⇒ 00:14:36.250 Zoran Selinger: Yeah.

113 00:14:36.250 ⇒ 00:14:40.110 Judd Kuehling: conversion rate goal that says if I opened that and then did something.

114 00:14:40.430 ⇒ 00:14:53.620 Judd Kuehling: afterwards, then it counts as a conversion. Well, you know, it’s, like, not really a conversion there, so for conversion rate, for SMS at least, I wouldn’t look at anything prior to August, but,

115 00:14:55.510 ⇒ 00:14:59.210 Judd Kuehling: And then… You see that jump in… in… in…

116 00:14:59.280 ⇒ 00:15:18.360 Judd Kuehling: in September, that’s kind of when we started doing it, so when we started doing it, you know, just like anything, no one’s seen a marketing SMS ever from the company. We start doing it, and it works pretty well, and then you kind of get some diminishing returns, right? Because people now have seen it a fair amount.

117 00:15:18.360 ⇒ 00:15:23.499 Judd Kuehling: So, that kind of makes sense, that it kind of starts in September really high, and then kind of…

118 00:15:23.640 ⇒ 00:15:27.030 Judd Kuehling: Works its way back to…

119 00:15:27.290 ⇒ 00:15:42.200 Judd Kuehling: back to, kind of, reality a bit, but yeah, that’s kind of my… as far as the email conversion rates, again, the volume went way up in end of August, September, and so that could be…

120 00:15:42.340 ⇒ 00:15:45.770 Judd Kuehling: Part of that conversion rate change, but it looks like it’s kinda…

121 00:15:45.770 ⇒ 00:15:49.589 Zoran Selinger: Oh yeah, of course, of course, they will be universed in most cases, yeah.

122 00:15:50.540 ⇒ 00:16:03.059 Zoran Selinger: Okay, so when it comes to SMS, what would you typically look as for… actually, let me first, before you answer, is my keyboard too loud when I’m typing?

123 00:16:03.690 ⇒ 00:16:04.110 Judd Kuehling: No.

124 00:16:04.110 ⇒ 00:16:04.850 Zoran Selinger: It’s not?

125 00:16:04.990 ⇒ 00:16:06.960 Zoran Selinger: Because I have a very loud keyboard.

126 00:16:06.960 ⇒ 00:16:07.750 Mitesh Patel: Hammer away.

127 00:16:07.750 ⇒ 00:16:12.130 Zoran Selinger: I’ll just write it on a piece of paper, I’ll just write my notes down.

128 00:16:12.130 ⇒ 00:16:13.679 Judd Kuehling: I don’t hear… I don’t hear anything.

129 00:16:13.680 ⇒ 00:16:15.360 Mitesh Patel: You’re fine, hammer away.

130 00:16:15.360 ⇒ 00:16:15.720 Judd Kuehling: Yeah.

131 00:16:15.720 ⇒ 00:16:18.589 Zoran Selinger: Okay, cool. So, before you go…

132 00:16:18.590 ⇒ 00:16:19.280 Judd Kuehling: farther.

133 00:16:19.280 ⇒ 00:16:21.530 Zoran Selinger: And what would you look at, as,

134 00:16:22.200 ⇒ 00:16:22.620 Judd Kuehling: et cetera.

135 00:16:22.620 ⇒ 00:16:23.829 Zoran Selinger: for SMS, then?

136 00:16:23.830 ⇒ 00:16:38.999 Judd Kuehling: So, yeah, so, just to, review. So, the conversion rate, the goal, conversion rate goal for email, just to step back for a second, is any open, so, and a customer opens, and then within 2 days, they convert.

137 00:16:39.000 ⇒ 00:16:52.970 Judd Kuehling: And that’s been the goal that was set up. I think Bobby and Mitesh agreed on that a while ago. That’s what we’ve been using in all emails going forward. I think some weren’t set up that way, I fixed it so it was, and so essentially every email should be like that.

138 00:16:52.980 ⇒ 00:16:59.230 Judd Kuehling: For SMS, well, obviously we can’t do that. We don’t know opens, we don’t measure opens, there’s no way to measure opens in SMS, so…

139 00:16:59.230 ⇒ 00:16:59.730 Zoran Selinger: Of course.

140 00:16:59.730 ⇒ 00:17:03.739 Judd Kuehling: We’re doing, right now, we have it.

141 00:17:04.369 ⇒ 00:17:06.089 Judd Kuehling: Any message sent.

142 00:17:06.829 ⇒ 00:17:16.380 Judd Kuehling: to the customer, and then 2 days within… of that message, received any conversion. So it’s a little bit softer of a conversion,

143 00:17:16.640 ⇒ 00:17:20.670 Judd Kuehling: Measurement, because it’s, just res… receipt of…

144 00:17:20.790 ⇒ 00:17:25.580 Judd Kuehling: SMS, we could change that to click, so we could force them to…

145 00:17:25.890 ⇒ 00:17:39.399 Judd Kuehling: the measurement to be the… the conversion rate is if they click, and then any… any conversion after that. It just will make the rate go down a bit, obviously, so I’m trying to get something as close to…

146 00:17:39.450 ⇒ 00:17:43.829 Zoran Selinger: comparison with email as I can, and that was the best way to do that.

147 00:17:43.830 ⇒ 00:17:47.770 Judd Kuehling: But yeah, that’s… that’s the background on that.

148 00:17:48.570 ⇒ 00:17:52.030 Zoran Selinger: So, once again, for email, it’s sent, and then within…

149 00:17:52.460 ⇒ 00:18:00.280 Judd Kuehling: Open, it’s open. So the email is opened, and within 2 days, the conversion event occurs.

150 00:18:00.280 ⇒ 00:18:04.410 Zoran Selinger: Sorry, that’s for email, and for SMS is… sense. Jeez.

151 00:18:04.410 ⇒ 00:18:04.970 Judd Kuehling: Received.

152 00:18:04.970 ⇒ 00:18:05.680 Zoran Selinger: 10th?

153 00:18:05.680 ⇒ 00:18:06.280 Judd Kuehling: Yeah.

154 00:18:07.310 ⇒ 00:18:11.829 Zoran Selinger: And then a conversion within 2 days. A transaction within 2 days?

155 00:18:11.830 ⇒ 00:18:12.210 Judd Kuehling: Right.

156 00:18:12.210 ⇒ 00:18:13.410 Zoran Selinger: Or something else.

157 00:18:13.410 ⇒ 00:18:20.840 Judd Kuehling: a transaction. Most of the… almost everywhere where I have a goal, it’s an order. So…

158 00:18:20.840 ⇒ 00:18:21.370 Zoran Selinger: Yeah.

159 00:18:21.370 ⇒ 00:18:23.870 Judd Kuehling: An order within 2 days.

160 00:18:24.340 ⇒ 00:18:25.000 Zoran Selinger: Okay.

161 00:18:25.390 ⇒ 00:18:33.940 Judd Kuehling: Now, remember, if I… if I get an SMS, and I look at it, and I go, oh yeah, I need to go… I want to go make an order from Eden.

162 00:18:34.210 ⇒ 00:18:38.590 Judd Kuehling: And I go, and I go to my… I see it on my phone.

163 00:18:38.700 ⇒ 00:18:45.370 Judd Kuehling: I don’t do anything, I don’t click or anything like that, but I see it on my phone. There’s no way that anyone knows that. There’s no way to measure that.

164 00:18:46.170 ⇒ 00:18:50.919 Judd Kuehling: So I see it on my phone, and then I go to my computer, and I go and I check out.

165 00:18:52.450 ⇒ 00:18:57.070 Judd Kuehling: that’s how we’re measuring that. So… if I…

166 00:18:57.420 ⇒ 00:19:03.620 Judd Kuehling: see it, and I take action somewhere else, or even take action on my phone without actually clicking on the SMS,

167 00:19:04.600 ⇒ 00:19:06.790 Judd Kuehling: That would be a conversion event.

168 00:19:07.330 ⇒ 00:19:13.870 Judd Kuehling: So that’s why we have it a little more broad like that. Like, there’s no way to, measure open, like I’m saying, so…

169 00:19:13.870 ⇒ 00:19:14.550 Zoran Selinger: Yeah.

170 00:19:14.550 ⇒ 00:19:18.529 Judd Kuehling: There’s no way to prove… there’s no way to know for sure whether I saw the SMS or not.

171 00:19:19.570 ⇒ 00:19:25.770 Zoran Selinger: Honestly, I’m even thinking if 2 days is a little bit too strict as well.

172 00:19:25.770 ⇒ 00:19:26.150 Judd Kuehling: Okay.

173 00:19:26.150 ⇒ 00:19:32.050 Zoran Selinger: I don’t know how we feel about Mitesha. How do we feel about… about 2 days only? .

174 00:19:32.050 ⇒ 00:19:36.890 Mitesh Patel: I think 2 days for email is sufficient, because…

175 00:19:37.260 ⇒ 00:19:44.570 Mitesh Patel: you know, once it ages, people don’t go back in their inbox and say, oh yeah, I got that email from Eden 5 days ago.

176 00:19:45.080 ⇒ 00:19:45.790 Judd Kuehling: Generally know.

177 00:19:45.790 ⇒ 00:19:52.540 Mitesh Patel: Yeah, so that’s why I think 2 days is… I mean, we can expand it to 3, but I wouldn’t go… there’s no need to go beyond 3.

178 00:19:52.750 ⇒ 00:20:09.009 Mitesh Patel: The other thing is, I know that we had previously defined, you know, once it’s opened, you know, 3 days, 2 days after it’s opened, but I don’t know, maybe we revisit that, right? And we say 3 days after it’s been sent.

179 00:20:09.360 ⇒ 00:20:15.260 Mitesh Patel: Because again, if people don’t open it in the first 24 hours, 48 hours, they’re not gonna open it.

180 00:20:15.540 ⇒ 00:20:18.710 Mitesh Patel: It’s dodging, right? It does happen.

181 00:20:18.780 ⇒ 00:20:19.560 Judd Kuehling: Yeah.

182 00:20:19.560 ⇒ 00:20:23.500 Mitesh Patel: Yeah, it’s rare, and they’re gonna be sort of very…

183 00:20:23.630 ⇒ 00:20:26.730 Mitesh Patel: You know, again, very rare outliers.

184 00:20:26.730 ⇒ 00:20:33.250 Judd Kuehling: So, what we want to do, maybe, and I want all your thoughts on it, and Greg, if this is an area that you have…

185 00:20:33.250 ⇒ 00:20:39.319 Mitesh Patel: a background in, would love to hear it as well. But in previous companies, you know, we did…

186 00:20:39.560 ⇒ 00:20:42.390 Mitesh Patel: 4 or 5 days from scent.

187 00:20:42.580 ⇒ 00:20:55.690 Mitesh Patel: And that’s the… we cap all email and SMS data, conversions, everything. We just pick a time, either it’s 4 or 5 days from time sent.

188 00:20:55.940 ⇒ 00:20:56.889 Mitesh Patel: And that’s it.

189 00:20:58.150 ⇒ 00:21:03.469 Mitesh Patel: So, that’s something we can define and agree to and, you know, make sure all the data reflects it.

190 00:21:04.090 ⇒ 00:21:10.540 Zoran Selinger: Yeah, yeah. I mean, my opinion would be for email, for example. We…

191 00:21:10.790 ⇒ 00:21:14.499 Zoran Selinger: If we have a stronger signal than send, And we do.

192 00:21:14.720 ⇒ 00:21:20.360 Zoran Selinger: Opens and clicks are stronger signals. We should… we should define a measure

193 00:21:20.490 ⇒ 00:21:31.469 Zoran Selinger: coming from the stronger signal, where if it’s a day from a click, or 3 days from an open, what… we should… we should do that. But for SMS, obviously, we don’t have

194 00:21:31.890 ⇒ 00:21:39.040 Zoran Selinger: that granularity, so, defining it from SEND is, is fine. Of course, we just,

195 00:21:39.420 ⇒ 00:21:49.760 Zoran Selinger: if you think, if you think two days is fine, it’s okay, yeah, it makes sense. I just wanted to see, how you think about it.

196 00:21:50.510 ⇒ 00:22:12.069 Greg Stoutenburg: If I could just jump in here. So, my thought here is that it makes sense to have this high-level reporting, like we’re talking about now, email versus SMS, and some kind of interaction back at the webpage. I think that makes perfect sense to sort of have a broad view. But then beyond that, we should look at individual goals and how they align with that. So, for example.

197 00:22:12.350 ⇒ 00:22:23.209 Greg Stoutenburg: overwhelmingly, most of your customers are on, sort of like the equivalent of a subscription, right? So they’re getting these reminders after a purchase, or because they’ve got a recurring thing coming up.

198 00:22:23.280 ⇒ 00:22:35.039 Greg Stoutenburg: So probably the kind of engagement that you’d expect for a customer if they receive that SMS or that email is they make some kind of edit to their cart. They add something, they take something off, they change a date.

199 00:22:35.060 ⇒ 00:22:45.699 Greg Stoutenburg: That kind of interaction. I think for email, when it’s a notice of something that’s coming up that’s recurring, I think open rate is going to be less valuable.

200 00:22:45.700 ⇒ 00:23:09.369 Greg Stoutenburg: One, because we know that we’ve got bots opening emails now and things like that. So, high level. We hope that that 45 is going to be artificially high, but we’re okay with that. If all of a sudden it dropped, then we’d have something to worry about. But as far as what the interaction looks like, you know, for example, I’m a Huel subscriber, have been since forever. I never opened that email. If I see it in the inbox, that’s all the signal I need, so…

201 00:23:09.370 ⇒ 00:23:22.020 Greg Stoutenburg: I don’t have to click it to go, oh, I actually do want to speed up my delivery by a week, I’m hungry. So, anyway, all this to say, I think that we should look at, on a campaign-by-campaign basis, what the goal of the campaign is.

202 00:23:22.130 ⇒ 00:23:29.609 Greg Stoutenburg: but still not dispose of this kind of high-level reporting. I still think it’s valuable to see what these levels of interaction look like across-channel.

203 00:23:30.830 ⇒ 00:23:31.680 Mitesh Patel: Sounds good.

204 00:23:32.570 ⇒ 00:23:38.289 Zoran Selinger: Yeah, so the next area is product performance. So this is just from,

205 00:23:38.630 ⇒ 00:23:49.850 Zoran Selinger: a campaign name, okay? So I tried to group this… group as much as… as much as possible, and these are, basically, these are the groups that we got.

206 00:23:50.030 ⇒ 00:23:53.640 Zoran Selinger: Obviously, semi is the… is the biggest,

207 00:23:53.770 ⇒ 00:23:57.180 Zoran Selinger: When it comes to the volume of messages.

208 00:23:57.450 ⇒ 00:24:04.309 Zoran Selinger: So, Judd, does kind of the order of these, that make sense to you?

209 00:24:04.310 ⇒ 00:24:04.900 Judd Kuehling: Yep.

210 00:24:05.320 ⇒ 00:24:11.020 Judd Kuehling: Yep, that looks what I would have expected, yeah. Although, Tertipatai has grown a lot.

211 00:24:11.420 ⇒ 00:24:14.249 Judd Kuehling: instance. Like, if you look at the last 3 months.

212 00:24:14.860 ⇒ 00:24:17.220 Judd Kuehling: It’s probably in second, but yeah.

213 00:24:17.700 ⇒ 00:24:21.890 Zoran Selinger: I think we… I got breakdowns for the first three,

214 00:24:22.040 ⇒ 00:24:31.219 Zoran Selinger: So, I don’t have a… I don’t have a breakdown for the Zapatite, but if you want, we can, we can look into that.

215 00:24:31.420 ⇒ 00:24:33.539 Judd Kuehling: But yeah, that makes sense, all that makes sense.

216 00:24:34.190 ⇒ 00:24:44.030 Zoran Selinger: So, the performance of SEMA is really, really good. So, that conversion rate and the click rates are really good, with, as you can see.

217 00:24:44.190 ⇒ 00:24:49.559 Zoran Selinger: The… the click rates, drop

218 00:24:50.540 ⇒ 00:24:57.550 Zoran Selinger: Kind of to, to the, to the levels of the first, first part of the year.

219 00:25:00.150 ⇒ 00:25:04.709 Zoran Selinger: So… Do you know, what happens here exactly?

220 00:25:05.350 ⇒ 00:25:06.319 Judd Kuehling: Yeah, again, this is, like.

221 00:25:06.320 ⇒ 00:25:09.929 Zoran Selinger: If you do anything significantly, in…

222 00:25:10.400 ⇒ 00:25:17.210 Judd Kuehling: the volume ramped up quite a bit. We weren’t sending messaging to…

223 00:25:17.400 ⇒ 00:25:29.819 Judd Kuehling: previous customers that had churned. We weren’t sending messages to, really, to abandoned… We were sending one kind of series to abandoned right after they abandoned, and then that was it. We gave up on them, and so we were…

224 00:25:30.030 ⇒ 00:25:31.680 Judd Kuehling: Going back.

225 00:25:31.820 ⇒ 00:25:38.340 Judd Kuehling: And, sending a kind of follow-up messaging to them. We’re sending messages to churn folks.

226 00:25:38.440 ⇒ 00:25:44.619 Judd Kuehling: So, obviously, all those are gonna have a lower conversion rate. The conversion, or the click rates…

227 00:25:45.230 ⇒ 00:25:52.690 Judd Kuehling: That you see that are so high are a lot of transactional, informational messaging

228 00:25:53.090 ⇒ 00:26:03.439 Judd Kuehling: first order, you know, first, kind of, delivery messaging, first order, those have really high engagement rates, right? And so…

229 00:26:03.440 ⇒ 00:26:04.140 Zoran Selinger: Of course, yeah.

230 00:26:04.140 ⇒ 00:26:17.040 Judd Kuehling: mixed into that, starting in, you know, September-ish, we started adding a lot of, kind of more marketing messages, and with that, you’re gonna have… naturally have, like, a lower engagement rate.

231 00:26:17.610 ⇒ 00:26:18.250 Zoran Selinger: Of course.

232 00:26:18.250 ⇒ 00:26:23.730 Mitesh Patel: Yeah, that’s why it’s critical to look at these by… message type.

233 00:26:24.550 ⇒ 00:26:25.290 Mitesh Patel: Right.

234 00:26:26.370 ⇒ 00:26:41.179 Zoran Selinger: Yeah, we’re gonna have, we’re gonna have a look at, the message type, again, what I could gather from the name of the campaign. So, we’ll have a look at that. Then the second one, Samoralin,

235 00:26:42.140 ⇒ 00:26:52.080 Zoran Selinger: Also, Really good, Click rate. Conversion rates, you see… The pattern is very similar.

236 00:26:53.920 ⇒ 00:26:55.410 Zoran Selinger: to, to Semma.

237 00:26:55.640 ⇒ 00:27:02.369 Zoran Selinger: You can see that, you can see that, big, big spike in, in volume, first on…

238 00:27:02.470 ⇒ 00:27:10.440 Zoran Selinger: In September, and then the drops in conversion rates, and con- and, click rates,

239 00:27:10.450 ⇒ 00:27:28.860 Zoran Selinger: Again, for both, I just want to say that when we look at the conversion rates, they are slightly growing in the last few months, so that’s a good… that’s a really good thing to see. So you did increase the volume, that did naturally drop conversion rates, but they are growing over the last few months, so that’s…

240 00:27:28.860 ⇒ 00:27:30.060 Judd Kuehling: They work.

241 00:27:30.060 ⇒ 00:27:38.839 Zoran Selinger: And especially, like, seeing it, seeing it, like, a stable growth, especially here with such a high-volume product, it’s, it’s really good.

242 00:27:39.040 ⇒ 00:27:49.700 Zoran Selinger: that will, like, that will show, a good increase. If we can continue doing that, that will be a really, really good, good highlight for, for, you know, next.

243 00:27:50.450 ⇒ 00:27:52.110 Zoran Selinger: next few months.

244 00:27:52.390 ⇒ 00:28:06.949 Judd Kuehling: Yeah, I mean, like Mitesh said, like, the click rates for transactional early, you know, customer messages are, you know, up near 8-9%, and the click rates for marketing messages, typically for e-com, would be, you know.

245 00:28:06.950 ⇒ 00:28:18.129 Judd Kuehling: 1% to 2%, and that’s typically what we’re seeing, maybe even lower than that for our marketing messages, and kind of the blend between those two is how we’re getting to these numbers here.

246 00:28:18.260 ⇒ 00:28:19.210 Judd Kuehling: So…

247 00:28:19.890 ⇒ 00:28:23.039 Zoran Selinger: Now, I’d be interested to see exactly how you measure that.

248 00:28:23.990 ⇒ 00:28:29.719 Zoran Selinger: Because what… what I had was just that… Email names, campaign names?

249 00:28:29.790 ⇒ 00:28:30.370 Judd Kuehling: Sure, sure.

250 00:28:30.370 ⇒ 00:28:36.619 Zoran Selinger: I could… I’d be interested to understand how you find those metrics.

251 00:28:37.740 ⇒ 00:28:54.689 Zoran Selinger: again, so these are… we see even higher conversion rates, very similar, very similar pattern, with maybe, a month, on the right side, one month change on the right side for NAD, and,

252 00:28:56.100 ⇒ 00:29:02.009 Zoran Selinger: The click rates are… you know, We don’t see any sharp…

253 00:29:02.150 ⇒ 00:29:09.919 Zoran Selinger: drop here, for this, but just a slow, slow, drop in, in click rates.

254 00:29:10.490 ⇒ 00:29:17.620 Zoran Selinger: Okay, so I understand we, to understand this, I really need to understand the split,

255 00:29:19.710 ⇒ 00:29:26.840 Zoran Selinger: So, basically, campaign type, plus… product.

256 00:29:27.100 ⇒ 00:29:27.850 Judd Kuehling: Yeah.

257 00:29:27.850 ⇒ 00:29:29.639 Zoran Selinger: Those breakdowns will be,

258 00:29:30.450 ⇒ 00:29:40.100 Zoran Selinger: much, much more interesting. So, these are the campaign types that I could see, from… just from the names of the campaigns.

259 00:29:40.800 ⇒ 00:29:46.840 Zoran Selinger: I understand that there probably is some… there probably is overlap between them.

260 00:29:47.090 ⇒ 00:29:58.780 Zoran Selinger: Maybe you could… you could merge a few of them. So the other… essentially, the other category is your… your welcome flow and a few of those things.

261 00:30:00.880 ⇒ 00:30:01.510 Judd Kuehling: Yeah.

262 00:30:01.510 ⇒ 00:30:18.050 Zoran Selinger: And you will see in the, in the next, next emails, and next slides that, we have pretty good performance for the… from the other category, and this is because those… those are those critical flows that are not specifically, marketing-related, right?

263 00:30:18.320 ⇒ 00:30:21.139 Judd Kuehling: Yeah, shipping, post-purchase, same.

264 00:30:21.640 ⇒ 00:30:23.700 Zoran Selinger: Activation, same.

265 00:30:24.020 ⇒ 00:30:24.340 Zoran Selinger: Yeah.

266 00:30:26.140 ⇒ 00:30:34.379 Judd Kuehling: Yeah, I guess Winback, it’s interesting, you have zero there. I… I don’t know that I called any campaigns Winback, I just put a WB in them.

267 00:30:34.380 ⇒ 00:30:44.819 Zoran Selinger: There is… there is one, or… I could find it… the reason why… why I grouped it this way is because I could find a few, a few examples.

268 00:30:45.010 ⇒ 00:30:46.079 Judd Kuehling: Okay, yeah, I can…

269 00:30:46.080 ⇒ 00:30:47.440 Zoran Selinger: I don’t know, yeah.

270 00:30:47.440 ⇒ 00:30:49.559 Judd Kuehling: Find these better if you want to… yeah.

271 00:30:49.870 ⇒ 00:30:55.300 Zoran Selinger: Yeah, so… Maybe what we could do, is…

272 00:30:56.330 ⇒ 00:31:15.980 Zoran Selinger: we should probably agree on the naming convention, which is a little bit tighter, so just to maybe have a category and subcategory, like, if you need to see a split, like, shipping, like, activation, post-purchase, right? Follow-up, those are all, like, post-purchase

273 00:31:16.590 ⇒ 00:31:20.270 Judd Kuehling: So… so there’s something called tags in.

274 00:31:20.270 ⇒ 00:31:24.639 Zoran Selinger: Oh, there isn’t Are you tagging your campaigns?

275 00:31:24.640 ⇒ 00:31:32.519 Judd Kuehling: Yeah, well, not… not UTM tags, but there’s a… internally, in Customer I.O, there’s something called tags, which is a little…

276 00:31:32.520 ⇒ 00:31:36.450 Zoran Selinger: Oh, yeah, no, that’s what I thought. Are you doing this already?

277 00:31:36.450 ⇒ 00:31:42.200 Judd Kuehling: Yeah, I’m using those tags as much as I can. There’s probably some historical stuff that needs to be cleaned up.

278 00:31:42.310 ⇒ 00:31:49.790 Judd Kuehling: But for stuff going forward, like, I have tags for abandoned cart, for win-back, for cross-sell, for…

279 00:31:50.360 ⇒ 00:31:53.510 Judd Kuehling: activation, I have tags by product.

280 00:31:53.760 ⇒ 00:32:00.810 Judd Kuehling: I have tags by, kind of, broader category. There’s tags by, kind of.

281 00:32:00.930 ⇒ 00:32:09.549 Judd Kuehling: little more event-type tags, like, when we brought back tersepatire, we had a tag for that, kind of, and things like that.

282 00:32:09.550 ⇒ 00:32:14.310 Zoran Selinger: I wonder why Henry didn’t use those, then, for your report on…

283 00:32:14.840 ⇒ 00:32:23.429 Judd Kuehling: Yeah, I don’t know. I know when we first talked about it, I was using, like, letter… letters in the names.

284 00:32:23.430 ⇒ 00:32:26.770 Zoran Selinger: Yeah, because he told me that he built that report.

285 00:32:26.770 ⇒ 00:32:27.200 Judd Kuehling: Yeah.

286 00:32:27.200 ⇒ 00:32:33.930 Zoran Selinger: based on the campaign name, this is why I took that approach. I never even checked for tags because of that.

287 00:32:34.070 ⇒ 00:32:45.869 Judd Kuehling: Yeah, I don’t know how… I’m assuming you can easily get that data out, because that’s the date… the tags are how I do… Mitesh do those reports that I show on Monday.

288 00:32:46.380 ⇒ 00:32:50.610 Judd Kuehling: I use those… I pull reporting by those tags for…

289 00:32:50.610 ⇒ 00:32:52.170 Zoran Selinger: Okay, excellent.

290 00:32:52.170 ⇒ 00:32:53.860 Judd Kuehling: groups, so, yeah.

291 00:32:54.230 ⇒ 00:32:56.739 Judd Kuehling: That might be a good place to start, at least.

292 00:32:56.880 ⇒ 00:32:58.140 Judd Kuehling: On some of this.

293 00:32:58.540 ⇒ 00:33:08.140 Mitesh Patel: So, Judd, those Monday reports, right? Yeah. And I know we talked about you redoing them a bit, right? Yeah. So I think it will help

294 00:33:08.370 ⇒ 00:33:14.090 Mitesh Patel: both, you know, Greg and Zaran, and you, If you redo that outline.

295 00:33:14.310 ⇒ 00:33:18.800 Mitesh Patel: You know, the granular metrics, as we call it. Okay. The way we talked about it.

296 00:33:18.930 ⇒ 00:33:21.269 Mitesh Patel: And then show them that sheet.

297 00:33:21.420 ⇒ 00:33:29.219 Mitesh Patel: And they will, I guarantee you, they can help you fill it out on a weekly basis, so you start with the data, and you can focus on the analysis.

298 00:33:29.530 ⇒ 00:33:30.150 Judd Kuehling: Okay.

299 00:33:31.140 ⇒ 00:33:32.870 Judd Kuehling: Yeah. I guess I need to…

300 00:33:33.430 ⇒ 00:33:40.619 Judd Kuehling: Yeah, I also… I want to, figure out exactly, kind of, because we talked about, on the campaign side, doing…

301 00:33:40.800 ⇒ 00:33:43.480 Judd Kuehling: Data by…

302 00:33:43.830 ⇒ 00:33:56.140 Judd Kuehling: treatment, and then buy our new, kind of, customer segments. I guess the treatment part I can figure out, but the customer segments, are we talking about

303 00:33:59.030 ⇒ 00:34:14.529 Judd Kuehling: defining who we think those messages are targeted to when we are talking about customer segments? Because we don’t really have the customer segment built into CIO today. Or is that something that we want to build, try and…

304 00:34:14.750 ⇒ 00:34:23.979 Judd Kuehling: Build in as a customer, like, define someone, everyone that comes into the list, define them as one of those customer segments as they come in, or something like that.

305 00:34:23.980 ⇒ 00:34:36.300 Mitesh Patel: Yeah, I think customer segments will be a little later for, for email, okay, for CIO. Okay. I think we start with type of message and treatment plan.

306 00:34:36.300 ⇒ 00:34:37.120 Judd Kuehling: Yeah, okay.

307 00:34:37.120 ⇒ 00:34:56.459 Mitesh Patel: And that will go very far into the level of data that will help. Now, as we learn about the customer personas, you know, that will be yet a different layer we can pile on top. But we need to be able to look at this data and optimize it along all of those dimensions.

308 00:34:56.630 ⇒ 00:34:58.889 Judd Kuehling: Yeah. Yep. Okay.

309 00:34:59.260 ⇒ 00:35:03.890 Judd Kuehling: Yeah, I can build that, and… or, you know, kind of rework that to,

310 00:35:04.080 ⇒ 00:35:06.030 Judd Kuehling: And show them, kind of, what I have.

311 00:35:06.460 ⇒ 00:35:06.990 Mitesh Patel: Yep.

312 00:35:07.830 ⇒ 00:35:13.450 Zoran Selinger: Yeah, and that’s also, Mitesh, going to be really, really useful for the KPI dash work.

313 00:35:13.690 ⇒ 00:35:14.899 Zoran Selinger: That we’re doing.

314 00:35:16.430 ⇒ 00:35:18.790 Zoran Selinger: So, yeah, I would like to see that.

315 00:35:19.490 ⇒ 00:35:27.130 Zoran Selinger: So, if you can see the conversion rates by that campaign type,

316 00:35:27.460 ⇒ 00:35:37.650 Zoran Selinger: the retention ones, so, obviously lower volume, but still, like, we have 117K messages in just the…

317 00:35:38.000 ⇒ 00:35:43.819 Zoran Selinger: those retention campaigns with a really high conversion rates. This is…

318 00:35:43.930 ⇒ 00:35:59.730 Zoran Selinger: really, really good. You know, Judd, you know exactly what you put into those campaigns and how they look like. I haven’t done any deep dives in terms of the messaging and the segments for those particular campaigns, but whatever

319 00:35:59.830 ⇒ 00:36:15.310 Zoran Selinger: you’re doing there, it’s, it’s really good. Obviously, we want to understand going forward exactly what’s being done then, and I know, Greg, especially, will have kind of questions and ideas, on there.

320 00:36:15.560 ⇒ 00:36:22.369 Zoran Selinger: Another kind of standout conversion rate is for follow-ups, as well, you can see.

321 00:36:22.710 ⇒ 00:36:26.989 Zoran Selinger: That follow-ups have really, also a higher conversion rate.

322 00:36:27.400 ⇒ 00:36:30.629 Zoran Selinger: But the retention really, really stands out.

323 00:36:33.960 ⇒ 00:36:39.509 Zoran Selinger: So what’s, what are the typical segments there? I would really like to, know, Judd.

324 00:36:40.470 ⇒ 00:36:41.490 Judd Kuehling: or…

325 00:36:41.770 ⇒ 00:36:46.749 Zoran Selinger: So these are campaigns that specifically have, I think, retention in them.

326 00:36:46.910 ⇒ 00:36:49.210 Zoran Selinger: In the campaign name.

327 00:36:49.390 ⇒ 00:36:55.750 Judd Kuehling: Yeah, I’m looking at that now. It looks like they were using that term, or maybe Bobby was using that term…

328 00:36:55.780 ⇒ 00:37:11.009 Judd Kuehling: for a lot of the, kind of, follow-up messaging, and that would be, like, messaging that says, like, you know, it’s time for you to, talk with a doctor to continue your treatment, and so, obviously, those…

329 00:37:11.320 ⇒ 00:37:15.170 Judd Kuehling: Should have pretty high conversion for people that, you know.

330 00:37:15.310 ⇒ 00:37:22.610 Judd Kuehling: want to continue to do that, which is most people. So… let me look at that, kind of how we use that term.

331 00:37:22.610 ⇒ 00:37:27.659 Zoran Selinger: Yeah. What those campaigns are. Okay. Because that really, really stands out.

332 00:37:27.660 ⇒ 00:37:35.780 Judd Kuehling: Yeah, I haven’t built campaigns with that word in it, so, these are all kind of ones that existed before I kind of started, and I’m looking at…

333 00:37:36.460 ⇒ 00:37:40.079 Judd Kuehling: Okay. That’s what they are, but we can… we can talk about that and figure that out.

334 00:37:40.940 ⇒ 00:37:54.769 Zoran Selinger: Yeah, yeah. So you just said something interesting that I don’t, don’t necessarily understand, especially on, like, I don’t really know exactly how, like, the health system in U.S. works, but…

335 00:37:55.580 ⇒ 00:38:03.649 Zoran Selinger: if I subscribe for 6 months, like, I have an active treatment for 6 months, and I want to continue

336 00:38:03.930 ⇒ 00:38:15.880 Zoran Selinger: With my subscription. Do I, again, need a, like, doctor’s, check, approval, whatever, for the next 6 months, or 3 months, or whatever the extension it is?

337 00:38:17.350 ⇒ 00:38:19.930 Zoran Selinger: So that needs to happen again, then.

338 00:38:20.380 ⇒ 00:38:22.390 Judd Kuehling: Yeah, okay.

339 00:38:22.390 ⇒ 00:38:38.340 Judd Kuehling: Natasha probably knows it even better than I do, but essentially, a customer gets a prescription length of, you know, 3, 6, or 12 months. That doesn’t mean when that ends that they’re not a customer anymore, it just means that they need to check in with their doctor to kind of

340 00:38:38.340 ⇒ 00:38:48.849 Judd Kuehling: renew their prescription, essentially out of refills. And so, to do that, they need to actually, check in with a doctor and kind of…

341 00:38:49.100 ⇒ 00:39:11.120 Judd Kuehling: the doctor needs to essentially see kind of where they’re at in their treatment in order to write a new prescription for the customer. Ideally, that’s kind of an easy process, and they… it’s quick, and then they continue to be on their monthly plan, continue to be billed monthly and delivered monthly, but it’s something that they need to actually take action on.

342 00:39:11.560 ⇒ 00:39:13.770 Zoran Selinger: Is that… do I have that right, Mitesh, kind of?

343 00:39:16.410 ⇒ 00:39:19.419 Mitesh Patel: Yeah, that’s right. Yeah, because…

344 00:39:20.050 ⇒ 00:39:38.319 Mitesh Patel: they do need to check in, and then there’s a follow-up at the… so there are two aspects of it, right? There’s a check-in, which is, how are you doing on your treatment? So there’s some questions we ask, and patients answer about their progress, and so on, right? If they’re having side effects, etc.

345 00:39:38.520 ⇒ 00:39:47.380 Mitesh Patel: And then a follow-up is, let’s say they had done a 3-month plan. At the end of the 3 months, they have to follow up, so it’s kind of sort of…

346 00:39:47.930 ⇒ 00:39:55.949 Mitesh Patel: A mini intake, sort of a check-in, but followed up with a checkout, where they have to credit… enter a credit card to sign up for a new plan.

347 00:39:56.490 ⇒ 00:39:57.160 Zoran Selinger: Okay.

348 00:39:57.630 ⇒ 00:40:05.230 Zoran Selinger: Okay, and this is why we essentially… and that’s gonna… that’s gonna tie into that conversation about

349 00:40:05.340 ⇒ 00:40:16.240 Zoran Selinger: reactivations and churn. This is why we’re not considering someone churned for, like, for the next 60 days after their last treatment ended, for example.

350 00:40:16.240 ⇒ 00:40:33.270 Judd Kuehling: Right. It can take some time, and so we give that… we create 60 days there. We also… there’s also people that kind of pause, or put on hold, and that can be because they’re traveling or something, or they’re… they have extra medication that they haven’t gotten through, and they need to pause their delivery for a bit.

351 00:40:33.440 ⇒ 00:40:35.020 Judd Kuehling: That we also…

352 00:40:35.440 ⇒ 00:40:41.119 Judd Kuehling: is another reason why we have that 60-day window. Generally, no one’s gonna go past that 60 days on the,

353 00:40:41.500 ⇒ 00:40:46.289 Judd Kuehling: On that, and so we… so we don’t really use, kind of,

354 00:40:46.690 ⇒ 00:40:59.400 Judd Kuehling: canceled or kind of non-canceled for what we consider churned. We actually use kind of whether they’ve ordered in the past 60 days, because the data in the past that we got for cancellation

355 00:40:59.460 ⇒ 00:41:09.200 Judd Kuehling: wasn’t really clear or clean. That may have changed since then. I know that we have, kind of, Zendesk data now for cancellation reason and things like that.

356 00:41:09.370 ⇒ 00:41:17.279 Judd Kuehling: So, that might need to be updated, but traditionally in the past, the way we defined churned people were people that didn’t have an order in 60 days.

357 00:41:17.900 ⇒ 00:41:20.029 Zoran Selinger: Yeah, okay, okay, and that…

358 00:41:20.320 ⇒ 00:41:31.050 Zoran Selinger: Zendesk information, Greg, is probably very important for segmentation and the type of campaigns we send for that particular segment.

359 00:41:31.620 ⇒ 00:41:32.430 Greg Stoutenburg: Great.

360 00:41:32.890 ⇒ 00:41:34.749 Zoran Selinger: Yeah, okay.

361 00:41:35.230 ⇒ 00:41:41.349 Zoran Selinger: So… we have… the unsubscribe rates

362 00:41:41.460 ⇒ 00:41:46.049 Zoran Selinger: Are, like, really stand out for abandoners and for follow-up campaigns?

363 00:41:46.780 ⇒ 00:41:52.470 Zoran Selinger: they are by far the highest out of all the other segments. Does that make sense?

364 00:41:52.470 ⇒ 00:41:58.010 Judd Kuehling: You’d expect to be high. These are people that came, looked for a price.

365 00:41:58.380 ⇒ 00:42:06.860 Judd Kuehling: went somewhere else, we’re sending them emails, and they’re… they’re kind of… they don’t want to get emails anymore.

366 00:42:06.860 ⇒ 00:42:07.300 Zoran Selinger: Oh.

367 00:42:07.300 ⇒ 00:42:08.550 Judd Kuehling: Why is that, though?

368 00:42:09.270 ⇒ 00:42:15.589 Zoran Selinger: Greg, what are our thoughts on the 1% unsubscribe rates?

369 00:42:16.470 ⇒ 00:42:19.210 Zoran Selinger: Doesn’t seem too… too high.

370 00:42:19.780 ⇒ 00:42:26.699 Greg Stoutenburg: No, that’s alright. Not concerning. Like, as far as, like, benchmarking, yeah, that’s not… that’s not too bad. Especially the… where we see it, in particular, as…

371 00:42:26.700 ⇒ 00:42:27.170 Zoran Selinger: Yeah.

372 00:42:27.170 ⇒ 00:42:40.039 Greg Stoutenburg: as, Ted noted, like, the abandoners, if they’re just clicking around… I mean, I’m in that group, you know, just exploring the website, seeing what’s out there, so these are tire kickers. For the follow-ups.

373 00:42:40.910 ⇒ 00:42:45.609 Greg Stoutenburg: I don’t want to be too quick to draw conclusions there, especially because that group is

374 00:42:45.750 ⇒ 00:42:59.340 Greg Stoutenburg: quite small compared to some of the others, as well as the fact that, you know, the… the issue raised already about tags by campaign, without a little more insight into what we’re calling a follow-up campaign, I think I want to be…

375 00:42:59.340 ⇒ 00:42:59.860 Zoran Selinger: Yeah.

376 00:42:59.860 ⇒ 00:43:01.690 Greg Stoutenburg: Reluctant to draw a conclusion.

377 00:43:02.030 ⇒ 00:43:15.410 Zoran Selinger: Yeah, just knowing that now we can… we can do things based on… on the labels, based on the tags that… that Judd is using, we can… we can do this, a little bit better, in terms of breakdowns.

378 00:43:15.590 ⇒ 00:43:28.669 Mitesh Patel: So, going back to, this is what I mean by, you know, message type. Certainly, these are by message type, but if we can look at it based on weekly, now we can identify not only, you know, yeah, we expect them to be high for abandoned.

379 00:43:28.820 ⇒ 00:43:34.760 Mitesh Patel: But are we seeing any trends in the bad… in a bad or good direction? And then we can do something about it.

380 00:43:36.360 ⇒ 00:43:42.130 Zoran Selinger: Excellent. I think that’s going to be really important for, for, us.

381 00:43:42.590 ⇒ 00:43:49.189 Zoran Selinger: Pulling, pulling, stats from, from, I.O. directly into the dash.

382 00:43:49.310 ⇒ 00:43:59.970 Zoran Selinger: So we’ll probably, Judd, we’ll have to do a cleanup on those, just make sure that everything, like, these tags are consistent.

383 00:43:59.970 ⇒ 00:44:01.020 Judd Kuehling: Yeah.

384 00:44:01.130 ⇒ 00:44:11.969 Zoran Selinger: So we can programmatically pull them from the API and report directly, so you don’t have to put the numbers in manually or anything, right? Yeah. We want to automate it as much as possible.

385 00:44:13.670 ⇒ 00:44:30.479 Zoran Selinger: The other… so the other… those are the key flows, right? The account creation of outcome flow are in there, especially, obviously, we… we want to see good, essentially, conversion rates from those, and that is exactly what we,

386 00:44:31.320 ⇒ 00:44:34.529 Zoran Selinger: exactly what we see.

387 00:44:35.070 ⇒ 00:44:42.870 Zoran Selinger: Yeah, but, so that category contains a lot. There’s probably, there’s probably, like.

388 00:44:43.850 ⇒ 00:44:51.500 Zoran Selinger: specific product-related stuff in there. There’s quite a few campaigns in that category, so it’s just a really, really big mix of

389 00:44:51.800 ⇒ 00:45:05.099 Zoran Selinger: a lot of the things that are… I just couldn’t gather, from the, from the campaign name where I would put it, so it just naturally ended in the… in the other category, but it’s a… it’s a big,

390 00:45:05.100 ⇒ 00:45:15.469 Zoran Selinger: big category, we can… we can obviously dig into it a little bit more. However, I think with looking into it by… by the tag, it’s gonna be,

391 00:45:15.720 ⇒ 00:45:22.530 Zoran Selinger: sold, like, cart abandoners, let’s see.

392 00:45:26.490 ⇒ 00:45:29.839 Zoran Selinger: Look at the conversion rates since August.

393 00:45:30.120 ⇒ 00:45:31.870 Zoran Selinger: That’s very interesting.

394 00:45:32.250 ⇒ 00:45:33.530 Zoran Selinger: So the…

395 00:45:34.350 ⇒ 00:45:35.000 Judd Kuehling: Yeah.

396 00:45:35.190 ⇒ 00:45:37.329 Judd Kuehling: That’s surprising to me.

397 00:45:37.330 ⇒ 00:45:37.910 Zoran Selinger: Yup.

398 00:45:39.980 ⇒ 00:45:55.199 Zoran Selinger: I’m really interested to see, if we… if we do it, if, if we do a report by your tags, if it’s still gonna look, like that, but it’s, it is, like, I included quite a… as you can see, it’s the biggest segment, right? So,

399 00:45:55.200 ⇒ 00:46:03.979 Zoran Selinger: I included a lot of campaigns in there, everything… those are pretty consistently tagged in the names, so I think this is gonna be…

400 00:46:04.050 ⇒ 00:46:09.349 Zoran Selinger: Right. You have… you mostly have, CA,

401 00:46:09.510 ⇒ 00:46:22.269 Zoran Selinger: And for the few… for a few campaigns, you actually have, like, cart abandonedness written, so I think that grouping here is… that’s pretty accurate. So, that conversion rate is going to be pretty accurate.

402 00:46:22.270 ⇒ 00:46:35.809 Judd Kuehling: Yeah, so I’ve done… obviously been doing testing in that group quite a bit. It’s a big group, so it’s worth doing it. I had some… a really big win real recently in one of my tests, but that would have been, like, more kind of January data.

403 00:46:36.270 ⇒ 00:46:40.230 Zoran Selinger: So it’s interesting to see, kind of, we saw the increases even before that.

404 00:46:40.350 ⇒ 00:46:41.610 Judd Kuehling: So yeah.

405 00:46:41.610 ⇒ 00:46:57.110 Zoran Selinger: It does, it does, so you have a pretty, pretty big jump, even for… even in December, but still, like, January data, which is not full January, obviously, it’s still there, and you see that the numbers are pretty high for January.

406 00:46:57.260 ⇒ 00:46:57.740 Judd Kuehling: Yeah.

407 00:46:57.740 ⇒ 00:47:05.000 Zoran Selinger: still. So, really good what those tests that you’re running look really good, even from this data.

408 00:47:05.150 ⇒ 00:47:14.200 Judd Kuehling: I remember, Mitesh, too, that December… no, end of November, December, January, we were on sale, and this group was getting all the sale

409 00:47:14.660 ⇒ 00:47:18.639 Judd Kuehling: Messaging, too, so that should improve conversion rate.

410 00:47:18.880 ⇒ 00:47:26.389 Judd Kuehling: In those months when we’re… $150 o f f in s t e a d o f$ 100, or now $120 o f f in s t e a d o f$ 100.

411 00:47:26.600 ⇒ 00:47:29.520 Judd Kuehling: Ideally, the conversion rate would be…

412 00:47:29.940 ⇒ 00:47:33.670 Judd Kuehling: better, naturally there, too, so that’s…

413 00:47:33.820 ⇒ 00:47:35.830 Judd Kuehling: Potentially part of this as well.

414 00:47:39.350 ⇒ 00:47:40.070 Mitesh Patel: Definitely.

415 00:47:42.560 ⇒ 00:47:49.759 Zoran Selinger: So this seems to me like a big opportunity, so post-purchase and, like, the shipping.

416 00:47:50.030 ⇒ 00:47:50.580 Judd Kuehling: How’s go.

417 00:47:50.580 ⇒ 00:48:06.620 Zoran Selinger: So, are we doing a lot of upsell, cross-sell in those, in those emails? Like, you see, like, the engagement rates are pretty, pretty good. Open rates are… are really high, it’s… it’s really a good opportunity to put, like, it’s prime real estate.

418 00:48:07.410 ⇒ 00:48:09.000 Zoran Selinger: Looks like it.

419 00:48:09.000 ⇒ 00:48:12.500 Judd Kuehling: So we were, more,

420 00:48:12.850 ⇒ 00:48:18.100 Judd Kuehling: in the past, we were doing some cross-sell there. The challenge was that…

421 00:48:18.400 ⇒ 00:48:25.899 Judd Kuehling: those actual cross-sell messages weren’t really successful. You know, I think it… in…

422 00:48:26.120 ⇒ 00:48:32.510 Judd Kuehling: I think in my mind, people trying to cross-sell a new treatment before someone’s even started their first treatment.

423 00:48:32.710 ⇒ 00:48:35.020 Judd Kuehling: I thought it was a bit of a stretch.

424 00:48:35.560 ⇒ 00:48:48.089 Judd Kuehling: And we saw that the conversion on those messages weren’t great. So I’ve decided to kind of move back a lot of the cross-selling messages to closer to, like, 30 days

425 00:48:48.670 ⇒ 00:48:50.889 Judd Kuehling: To 60, 90 days.

426 00:48:51.170 ⇒ 00:48:55.900 Judd Kuehling: To get… allow the customers to try their, kind of, first treatment first.

427 00:48:56.540 ⇒ 00:49:00.830 Judd Kuehling: for a couple weeks before we start talking about that kind of stuff. We could continue.

428 00:49:00.830 ⇒ 00:49:01.280 Zoran Selinger: development.

429 00:49:01.280 ⇒ 00:49:13.180 Judd Kuehling: there, because the engagement, like you said, is high. It’s just, like, it’s a challenge, especially in the summer when we had delivery issues, trying to sell things to people before they even got there.

430 00:49:13.340 ⇒ 00:49:16.739 Judd Kuehling: Their first product, while they were still waiting for it to be shipped to them.

431 00:49:17.730 ⇒ 00:49:21.640 Judd Kuehling: And that’s kind of what we were doing, and so I was, you know, we saw…

432 00:49:21.860 ⇒ 00:49:25.030 Judd Kuehling: some pretty weak engagement in those emails, and so I was just.

433 00:49:25.030 ⇒ 00:49:25.780 Zoran Selinger: Exciting. Oh, okay.

434 00:49:25.780 ⇒ 00:49:31.230 Judd Kuehling: trying to test deeper into the life cycle there. But, so…

435 00:49:31.230 ⇒ 00:49:42.140 Zoran Selinger: Most of these categories, like post-purchase and shipping, this is still them not completing the process, right? They’re still not… they’re mostly not received, they’re… they’re… they’re.

436 00:49:42.140 ⇒ 00:49:53.789 Judd Kuehling: Post-purchase, they kind of get… they start to get close to being delivered at the kind of end of that post-purchase campaign. Delivery, obviously, is, is them receiving the product. I think, like.

437 00:49:54.060 ⇒ 00:49:56.260 Judd Kuehling: Mitesh, I think this is where we could kind of…

438 00:49:56.440 ⇒ 00:50:03.720 Judd Kuehling: strategize around, kind of, a lower lift upsell? Like, when we eventually talk about…

439 00:50:03.830 ⇒ 00:50:16.640 Judd Kuehling: you know, nutraceuticals, or protein powders, or, you know, things that are smaller, and not trying to kind of say, hey, go buy NAD. You haven’t finished starting your GLP-1 yet, but here’s, you know…

440 00:50:16.640 ⇒ 00:50:30.669 Mitesh Patel: Yeah, agreed, agreed. Even, even, like, look, the timing on all of those upsells was just off. It was wrong. Right. Right? And we got, again, it’s gotta be more about their journey and experiences they’re going through.

441 00:50:30.670 ⇒ 00:50:43.409 Mitesh Patel: Right. Right now, Zoran and Greg, we don’t get check-in data, right? We don’t, like, if a customer checks in and says, hey, I don’t have any side effects, or if they say, yeah, I’m feeling nausea.

442 00:50:43.720 ⇒ 00:50:54.919 Mitesh Patel: we should be able to target that customer and send them a, hey, here’s what you do for nausea, or you’re feeling, you know, out of energy, here’s what to do to regain energy. And then the cross-sell

443 00:50:55.140 ⇒ 00:50:58.379 Mitesh Patel: It’s secondary, but will be more effective.

444 00:50:58.600 ⇒ 00:51:14.829 Mitesh Patel: Right? So, if a person is feeling out of energy, then we say, hey, you know what our other patients do when they’re feeling out of energy? Is, you know, they use MYCB12, or they increase their protein intake, right? And we’re not…

445 00:51:14.920 ⇒ 00:51:18.749 Mitesh Patel: Inherently trying to sell them something, or upsell them something.

446 00:51:18.860 ⇒ 00:51:24.609 Mitesh Patel: What we’re inherently trying to do is improve their experience with the GLP-1 journey.

447 00:51:25.430 ⇒ 00:51:26.580 Judd Kuehling: problem is.

448 00:51:26.800 ⇒ 00:51:29.200 Mitesh Patel: Right, so if we do that effectively.

449 00:51:29.580 ⇒ 00:51:37.259 Mitesh Patel: Then, both their adherent, you know, their cross-sell volume goes up.

450 00:51:37.900 ⇒ 00:51:42.329 Mitesh Patel: Their adherence improves, so they’re… they get to their outcomes better.

451 00:51:42.470 ⇒ 00:51:48.430 Mitesh Patel: Right? Otherwise, if they feel nausea or constipated, they’ll skip a dose or whatever. And their retention improves.

452 00:51:49.630 ⇒ 00:51:51.800 Mitesh Patel: This is the way to…

453 00:51:52.520 ⇒ 00:52:02.890 Mitesh Patel: significantly more, you know, better outcomes and experience for the customer, and for us, higher retention, higher LTV, higher cross-sell.

454 00:52:04.040 ⇒ 00:52:07.919 Mitesh Patel: This is what we have to evolve lifecycle marketing to.

455 00:52:09.010 ⇒ 00:52:09.780 Mitesh Patel: Right?

456 00:52:11.280 ⇒ 00:52:24.210 Mitesh Patel: Okay. And so the things that we’re talking about here are really, really important, right? But this is all, you know, looking in the rearview mirror, right? This is about, hey, let’s look at what trends there are and what we can improve.

457 00:52:24.610 ⇒ 00:52:28.359 Mitesh Patel: Once we have… and this comment applies to every single channel.

458 00:52:28.730 ⇒ 00:52:32.220 Mitesh Patel: Once we have this stuff in place, Right?

459 00:52:32.400 ⇒ 00:52:34.159 Mitesh Patel: then I want your help.

460 00:52:34.850 ⇒ 00:52:44.010 Mitesh Patel: the data team to say, how do we start looking, you know, ahead through the windshield, and say, what’s next? What can we do? What can we predict?

461 00:52:44.770 ⇒ 00:52:48.919 Mitesh Patel: You know, when can we predict churn? We have so much data.

462 00:52:49.180 ⇒ 00:52:52.800 Mitesh Patel: We just… Sitting there, waiting to be leveraged.

463 00:52:54.740 ⇒ 00:53:13.269 Zoran Selinger: Excellent. Like, this is exactly what I wanted from… from this. I knew that maybe some of the… some of the slides are not gonna be there, but I want to actually have that conversation, where I can finally understand what exactly is happening here, and where we want to go. Excellent.

464 00:53:13.380 ⇒ 00:53:14.689 Zoran Selinger: We have only…

465 00:53:15.000 ⇒ 00:53:26.400 Zoran Selinger: 9 minutes left, so I would like… so, Judd is, is familiar with the reports that Henry created for him, so let’s not comment on that too much.

466 00:53:26.540 ⇒ 00:53:35.370 Zoran Selinger: Let’s have this conversation that we need to have, about how we define churn and reactivations. So…

467 00:53:36.410 ⇒ 00:53:39.709 Zoran Selinger: I… Judd, I sent you these slides before.

468 00:53:40.310 ⇒ 00:53:47.080 Zoran Selinger: we… I… so what I propose… what I propose here is that we’ll look at

469 00:53:48.330 ⇒ 00:53:50.740 Zoran Selinger: That we look at…

470 00:53:50.910 ⇒ 00:54:09.420 Zoran Selinger: the status of a customer based on the treatments, not the transactions, but treatments. So I initially… I initially started calculating churn rates based on transactions, and then I found out that it’s… there’s much more complexity in it.

471 00:54:09.730 ⇒ 00:54:14.810 Zoran Selinger: that I actually need to understand, that I actually need to understand

472 00:54:15.700 ⇒ 00:54:26.069 Zoran Selinger: how they are with treatments at the moment, what are the status of their treatments, and when I started looking at treatments, then essentially the picture, to me, became pretty clear.

473 00:54:27.170 ⇒ 00:54:40.860 Zoran Selinger: So, a churn car… and I initially, asked you, Judd, about, like, 30 days. I initially said 30 days, but then you moved it to 60. So, I would say that

474 00:54:40.980 ⇒ 00:54:52.369 Zoran Selinger: A churned customer is any customer that previously had at least one active treatment. So, it was, at some point, an active customer.

475 00:54:52.720 ⇒ 00:55:02.470 Zoran Selinger: And then… That customer, for at least 60 days, doesn’t have any active treatments.

476 00:55:03.040 ⇒ 00:55:06.830 Zoran Selinger: So, means that the last active,

477 00:55:07.620 ⇒ 00:55:10.750 Zoran Selinger: treatment that I had is either completed or canceled.

478 00:55:11.860 ⇒ 00:55:14.660 Judd Kuehling: Yeah, okay. Yeah, I mean, I think…

479 00:55:14.660 ⇒ 00:55:19.960 Zoran Selinger: If they are at that state for… maybe they started, like, maybe they even have a pending…

480 00:55:20.460 ⇒ 00:55:29.740 Zoran Selinger: I still never included those. If they… they were active, and they… they don’t have any active ones for 60 days, that’s a churn customer.

481 00:55:30.830 ⇒ 00:55:48.769 Judd Kuehling: Yeah, I think that the challenge of this, and kind of where it came up in the past, was the definition of active. So, if my credit card fails, and I’m, like, frustrated, I just leave, and my treatment still looks like it’s active, but I’m just not paying, and I just kind of disappear. If my.

482 00:55:48.770 ⇒ 00:55:52.159 Zoran Selinger: is… in the database,

483 00:55:52.500 ⇒ 00:55:53.929 Judd Kuehling: Yeah, I don’t know, kind of, how those are.

484 00:55:53.930 ⇒ 00:55:55.169 Zoran Selinger: Yeah, let’s try to…

485 00:55:55.170 ⇒ 00:55:56.379 Judd Kuehling: bind, I guess.

486 00:55:57.980 ⇒ 00:56:05.490 Zoran Selinger: We need to see. So, I think the status will… of that particular treatment will be pending.

487 00:56:06.090 ⇒ 00:56:10.180 Judd Kuehling: Okay. I mean, that’s why we’ve used orders, because…

488 00:56:10.360 ⇒ 00:56:17.019 Judd Kuehling: If you’re gonna be a member, you have to continue to order, or, you know, the orders are automated, but you have to continue to…

489 00:56:17.360 ⇒ 00:56:22.309 Judd Kuehling: Have an order, and so that’s why we’re using orders as the definition, because…

490 00:56:22.890 ⇒ 00:56:29.229 Judd Kuehling: A customer not doing anything, and staying active… staying technically active, because they haven’t actually done anything, they’re just kind of…

491 00:56:29.410 ⇒ 00:56:36.759 Judd Kuehling: disappeared, or whatever, like, I think that there’s issues that could come up where they’re not getting orders.

492 00:56:37.110 ⇒ 00:56:39.669 Judd Kuehling: But they’re still considered active, I guess.

493 00:56:39.970 ⇒ 00:56:40.560 Zoran Selinger: Yeah.

494 00:56:41.380 ⇒ 00:56:46.760 Zoran Selinger: And just here, we can see that, so…

495 00:56:46.910 ⇒ 00:56:52.150 Zoran Selinger: Obviously, I joined a little bit late, There were price increases.

496 00:56:52.450 ⇒ 00:56:55.680 Zoran Selinger: in… February, March, right?

497 00:56:57.160 ⇒ 00:57:01.849 Judd Kuehling: Yeah, that I’m not super familiar with, Mitesh, mine, no.

498 00:57:03.000 ⇒ 00:57:03.920 Zoran Selinger: Mitesh?

499 00:57:03.920 ⇒ 00:57:07.430 Mitesh Patel: Yeah, I don’t… February and March.

500 00:57:07.430 ⇒ 00:57:25.149 Zoran Selinger: customers in February and March. So this is, like, a really significant event, whatever happened then, exactly. I can see from the campaigns that we had campaigns, about prices… price increases back then.

501 00:57:25.790 ⇒ 00:57:26.330 Judd Kuehling: I thought…

502 00:57:26.330 ⇒ 00:57:27.569 Mitesh Patel: Now, what we did…

503 00:57:27.570 ⇒ 00:57:28.240 Judd Kuehling: the diet.

504 00:57:28.750 ⇒ 00:57:43.879 Mitesh Patel: Yeah, we stopped Trezepatide that in February and March, and that was going to be the big reason for it. I know there were price increases, because in January, actually middle of January, we introduced 3-, 6-, and 12-month plans.

505 00:57:44.710 ⇒ 00:57:48.050 Mitesh Patel: And increase the price of the one-month plan.

506 00:57:48.160 ⇒ 00:57:49.479 Mitesh Patel: And so…

507 00:57:50.450 ⇒ 00:58:09.270 Mitesh Patel: Then, after collecting some data at the end of February, early March, we kind of went back to, you know, repricing the one-month plan where it was before. So the churn, again, this is, like, you know, the churn is defined as 60 days, so based on that, this would have been from before the price decrease.

508 00:58:13.160 ⇒ 00:58:14.249 Mitesh Patel: You know what I mean?

509 00:58:18.890 ⇒ 00:58:22.069 Zoran Selinger: Yeah, so, yeah, yeah, so they became…

510 00:58:23.760 ⇒ 00:58:29.100 Zoran Selinger: Became an active customer before the price decrease.

511 00:58:29.600 ⇒ 00:58:34.480 Zoran Selinger: And then, at this point, they needed to reactivate, and they didn’t.

512 00:58:34.920 ⇒ 00:58:35.850 Zoran Selinger: Right.

513 00:58:35.850 ⇒ 00:58:42.380 Mitesh Patel: And that could have been because we stopped selling TERS, we stopped selling oral, there was a lot of changes we made.

514 00:58:43.040 ⇒ 00:58:44.610 Mitesh Patel: Yeah, I wouldn’t…

515 00:58:44.610 ⇒ 00:58:49.279 Zoran Selinger: Most due to the offers changing, not necessarily price.

516 00:58:49.620 ⇒ 00:58:53.700 Mitesh Patel: Yeah, I would ignore those, spikes in February and March.

517 00:58:55.290 ⇒ 00:59:02.279 Mitesh Patel: Really, the data after May, and even in May, is kind of, we change to personalized plans.

518 00:59:02.280 ⇒ 00:59:08.510 Zoran Selinger: Like, compounded SEMA, comp… and then later we reintroduced compounded TERS 5 months ago.

519 00:59:08.650 ⇒ 00:59:15.359 Mitesh Patel: So, yeah, some of this data… Okay. There’s too many… Too many changes.

520 00:59:15.640 ⇒ 00:59:16.549 Mitesh Patel: You know?

521 00:59:16.990 ⇒ 00:59:25.650 Zoran Selinger: Okay, cool, I understand. That’s exactly what I wrote down, that it’s just too many changes, so it’s really, really hard to isolate.

522 00:59:25.690 ⇒ 00:59:26.490 Mitesh Patel: Isolate.

523 00:59:26.770 ⇒ 00:59:34.289 Mitesh Patel: Hey guys, I gotta jump, someone’s here to meet, sorry, you guys, you know, we’ll catch up if there’s anything I miss, right? Thank you.

524 00:59:34.290 ⇒ 00:59:35.300 Greg Stoutenburg: Okay. Thanks, Vitesh. See ya.

525 00:59:35.300 ⇒ 00:59:36.000 Zoran Selinger: Thanks.

526 00:59:38.400 ⇒ 00:59:40.470 Zoran Selinger: So, Judd,

527 00:59:40.810 ⇒ 01:00:03.820 Zoran Selinger: So, the reactivations, so that would be anyone that were, as you can see from the length of the reactivation, we’re not counting anything prior to 60 days of them not having an active treatment. So it’s anyone who doesn’t have an active treatment for at least 60 days, and then they have a next active treatment.

528 01:00:03.820 ⇒ 01:00:12.450 Zoran Selinger: Yeah. Okay? Then… they reactivated, whatever. Maybe an existing treatment, or a new treatment, doesn’t matter. It’s any…

529 01:00:12.960 ⇒ 01:00:16.559 Zoran Selinger: Any customer that previously was active, then

530 01:00:16.900 ⇒ 01:00:23.060 Zoran Selinger: They were inactive for 60 days, and then they became active again.

531 01:00:23.360 ⇒ 01:00:27.399 Judd Kuehling: Got it, that makes sense. I mean, I think you’re gonna get people that are more recent.

532 01:00:27.880 ⇒ 01:00:43.459 Zoran Selinger: Yeah, yeah. I mean, yeah, that’s exactly as per your request. I think that makes sense. Let’s do that, that’s fine. So, you even managed to reactivate a few people that were inactive for over a year. Yeah.

533 01:00:43.460 ⇒ 01:00:49.040 Zoran Selinger: So that’s really interesting. As you can see, June was the biggest.

534 01:00:49.100 ⇒ 01:00:54.160 Zoran Selinger: In terms of reactivations, the volume of reactivations

535 01:00:54.380 ⇒ 01:00:56.169 Zoran Selinger: Does that make sense to you?

536 01:00:56.720 ⇒ 01:01:04.010 Judd Kuehling: Yeah, I think so. Like, I started in August, and I started immediately on, kind of, the reactivation.

537 01:01:04.120 ⇒ 01:01:05.790 Judd Kuehling: work.

538 01:01:05.930 ⇒ 01:01:08.670 Judd Kuehling: And so, we sent out a bunch of…

539 01:01:09.830 ⇒ 01:01:16.330 Judd Kuehling: messages in August and kind of catch up. Essentially, like, when I started sending out messages, it’s like, I grabbed everybody in the past.

540 01:01:17.210 ⇒ 01:01:25.620 Judd Kuehling: I built a campaign built that I grabbed everybody in the past, and then it kind of grabbed new people, new churned people, as it went on, so obviously you’re gonna have.

541 01:01:25.620 ⇒ 01:01:26.220 Zoran Selinger: Oh, yeah.

542 01:01:26.220 ⇒ 01:01:33.809 Judd Kuehling: a spike in August, and then it’s gonna tail off, so that’s kind of what we’re seeing there. Anything before that, like, I don’t understand the June data.

543 01:01:33.810 ⇒ 01:01:34.249 Zoran Selinger: Yeah, of course.

544 01:01:34.250 ⇒ 01:01:35.670 Judd Kuehling: We also got that, but…

545 01:01:35.900 ⇒ 01:01:38.860 Greg Stoutenburg: I notice on a lot of charts, June is a weird month.

546 01:01:38.860 ⇒ 01:01:39.300 Judd Kuehling: Yeah.

547 01:01:39.300 ⇒ 01:01:39.930 Greg Stoutenburg: In many ways.

548 01:01:39.930 ⇒ 01:01:40.350 Zoran Selinger: Yeah.

549 01:01:40.350 ⇒ 01:01:41.499 Greg Stoutenburg: aren’t share it today.

550 01:01:41.500 ⇒ 01:01:49.150 Judd Kuehling: There was a lot of weird things. I went in spring and early summer last year that kind of changes to the company, changes to the pricing, changes to the…

551 01:01:49.260 ⇒ 01:01:56.959 Judd Kuehling: the format of the way they were selling things and things like that, so I don’t know all the details, but yeah, some of that data’s gonna be a little weird.

552 01:01:58.170 ⇒ 01:02:05.409 Zoran Selinger: Cool. And I wanted to talk about the never-active, people. So…

553 01:02:05.660 ⇒ 01:02:11.650 Zoran Selinger: In my analysis, this is one of the… this is what I had… active. I had…

554 01:02:11.680 ⇒ 01:02:20.929 Zoran Selinger: inactive people. I have recently inactive people, which are basically those that just stopped having an active treatment.

555 01:02:20.930 ⇒ 01:02:33.690 Zoran Selinger: But we’re still within those 60 days. That’s recently inactive. But we had people that started going through, that activated a treatment, and at some point, the treatment was pending.

556 01:02:35.750 ⇒ 01:02:43.059 Zoran Selinger: And it never became an active treatment. So, we have them in the system. They have a started treatment.

557 01:02:43.270 ⇒ 01:02:49.629 Zoran Selinger: But never any of the treatments that I had, none of them ever became active.

558 01:02:49.850 ⇒ 01:02:50.200 Judd Kuehling: God.

559 01:02:50.200 ⇒ 01:02:55.179 Zoran Selinger: There are a number of active people. We have almost 40,000 of them.

560 01:02:55.180 ⇒ 01:03:13.660 Judd Kuehling: Got it. So that’s the data that I was confused about, because my number looks a lot bigger than that, but I’m looking at just people that gave us their email. They didn’t even start, like, an active treatment. It didn’t… it didn’t go… like, they never got to the point that you’re talking about. Now I understand why your number is smaller. Ideally, you’re…

561 01:03:13.710 ⇒ 01:03:20.010 Judd Kuehling: I don’t really have a way… I currently didn’t have a way to look at those people specifically, but.

562 01:03:20.010 ⇒ 01:03:20.450 Zoran Selinger: That’s brilliant.

563 01:03:20.450 ⇒ 01:03:26.919 Judd Kuehling: what you’re saying, those people should have a higher engagement when we’re trying to get them back, because they kind of went farther down the process, or whatever.

564 01:03:26.920 ⇒ 01:03:45.600 Zoran Selinger: So much harder, if you ask me, that’s so much harder. This is a much more qualified audience than they gave us their email, and that’s what we have. This is much, much more qualified audience, and really important, and kind of that ties us into…

565 01:03:45.600 ⇒ 01:03:48.589 Zoran Selinger: Do we have a little bit more time, Judd? Yeah, yeah.

566 01:03:48.590 ⇒ 01:03:49.180 Judd Kuehling: I’m gonna…

567 01:03:49.180 ⇒ 01:03:51.119 Zoran Selinger: few minutes more.

568 01:03:51.330 ⇒ 01:03:52.000 Judd Kuehling: But I would love to have.

569 01:03:52.000 ⇒ 01:03:52.430 Zoran Selinger: Why’s not…

570 01:03:52.430 ⇒ 01:03:56.270 Judd Kuehling: have that definition in CIO. I don’t believe…

571 01:03:56.570 ⇒ 01:03:57.969 Judd Kuehling: That I have a way to…

572 01:03:57.970 ⇒ 01:03:59.169 Zoran Selinger: what I want to talk about.

573 01:03:59.170 ⇒ 01:03:59.940 Judd Kuehling: Yeah, awesome.

574 01:03:59.940 ⇒ 01:04:11.209 Zoran Selinger: And Greg, also, if you can jump in here. So right now, what I could see, like, you have almost 800 segments defined in Customer I.O.

575 01:04:11.650 ⇒ 01:04:14.780 Zoran Selinger: There are most… there are at least…

576 01:04:15.540 ⇒ 01:04:22.910 Zoran Selinger: I took a sample of 20, 30 of them. They’re all created from events that Customer I.O. has.

577 01:04:23.580 ⇒ 01:04:24.690 Zoran Selinger: Right?

578 01:04:27.030 ⇒ 01:04:34.829 Zoran Selinger: we have a huge data warehouse, right? Where we could pull any possible segment that we could think of.

579 01:04:35.870 ⇒ 01:04:50.579 Zoran Selinger: like this one, which is just… which is maybe nev… which is never gonna be possible from sending signals to customer I.O, because a lot of things are maybe, like, backend processes, stuff like that. So…

580 01:04:51.420 ⇒ 01:04:53.960 Zoran Selinger: You do not currently have a process of

581 01:04:54.480 ⇒ 01:05:00.710 Zoran Selinger: Someone defining an email list for you on the backend, and importing it.

582 01:05:00.860 ⇒ 01:05:05.289 Zoran Selinger: importing it into Customer I.O. Do you have something like that?

583 01:05:05.290 ⇒ 01:05:05.880 Judd Kuehling: No.

584 01:05:06.390 ⇒ 01:05:08.620 Zoran Selinger: Okay, okay. Greg.

585 01:05:08.620 ⇒ 01:05:18.130 Judd Kuehling: I think anything really outside of Customer I.O. I had Henry adding some fields based on data from segment, here and there, but it was really kind of…

586 01:05:18.310 ⇒ 01:05:22.939 Judd Kuehling: Ad hoc, and kind of just little things, and we haven’t really done, like, a deep dive into it.

587 01:05:25.040 ⇒ 01:05:27.760 Zoran Selinger: Greg, would that be a normal workflow?

588 01:05:28.410 ⇒ 01:05:29.600 Greg Stoutenburg: So,

589 01:05:29.600 ⇒ 01:05:36.680 Zoran Selinger: Judd has an idea about a segment, we define it, we pull an email list, we import it, and then he can use it.

590 01:05:37.030 ⇒ 01:05:45.529 Greg Stoutenburg: Yes, it… that is a normal workflow, and, I mean, it sounds like you’ve taken some steps in this direction previously. The,

591 01:05:45.810 ⇒ 01:06:02.180 Greg Stoutenburg: the approach to take here is just whichever one suits your needs better, right? If the data is not already in Customer I.O, then let’s get the right data in there, let’s get the right triggers in there. So yeah, I mean, certainly we can do those things, and I’ve worked for clients and had full-time roles in the past where

592 01:06:02.180 ⇒ 01:06:14.620 Greg Stoutenburg: sometimes we did that sort of thing often, and, to test out some ad hoc campaign, and then… but then there were other times where we went, actually, there’s just some particular event that we need to make sure is added to customer I.O.

593 01:06:14.620 ⇒ 01:06:27.360 Greg Stoutenburg: And, you know, you pipe in the data, it’s always there, now you don’t have to look outside again for the campaign. So, and this sort of goes back to what I was asking a little bit ago about a customer journey map. I think as long as we’ve got… as long as we’ve got,

594 01:06:27.370 ⇒ 01:06:39.599 Greg Stoutenburg: campaigns with aligned goals and an understanding of how we’re trying to move folks from one place to another, then, you know, we make sure that’s all ironed out, and then we go, alright, what are the right data sources?

595 01:06:40.730 ⇒ 01:06:41.750 Greg Stoutenburg: And go from there.

596 01:06:41.750 ⇒ 01:06:48.499 Zoran Selinger: Okay, so the idea is to… to make… Us pulling the lists?

597 01:06:49.170 ⇒ 01:06:54.449 Zoran Selinger: as… Not an often, so we don’t have to do it often.

598 01:06:54.660 ⇒ 01:07:00.060 Zoran Selinger: Sorry, my son is… He’s making noise next to me, just trying to get some papers.

599 01:07:00.260 ⇒ 01:07:15.359 Zoran Selinger: Okay, so we… we should… we should try as much as possible to allow Judd to have enough signals so he can create any segment that he wants inside the platform, but if that’s…

600 01:07:15.580 ⇒ 01:07:20.980 Zoran Selinger: If that’s not possible, we should have a system where we can pull something for him.

601 01:07:21.290 ⇒ 01:07:22.949 Zoran Selinger: If we need to.

602 01:07:23.290 ⇒ 01:07:24.080 Zoran Selinger: Yes.

603 01:07:24.210 ⇒ 01:07:29.070 Zoran Selinger: Okay, okay, alright, alright, that makes sense.

604 01:07:30.580 ⇒ 01:07:32.289 Zoran Selinger: John, do you have…

605 01:07:33.210 ⇒ 01:07:42.989 Zoran Selinger: questions, comments? Do you want a particular drill down into any of those segments? Right now?

606 01:07:44.090 ⇒ 01:07:51.259 Judd Kuehling: No, I think this is great, like, you know, when we talk about adding stuff to Customer I.O, I don’t know that we have, like, a limit on…

607 01:07:53.460 ⇒ 01:08:04.160 Judd Kuehling: fields there, so, I mean, like, for example, like, I don’t really feel the need to do, like, ad hoc lists to Customer I.O. We can create a field…

608 01:08:04.560 ⇒ 01:08:10.659 Judd Kuehling: that’s called, you know, whatever it’s called, Never Active Group 1, or whatever it is, you know, and then…

609 01:08:10.780 ⇒ 01:08:12.259 Judd Kuehling: Defi- and then, like.

610 01:08:12.730 ⇒ 01:08:20.049 Judd Kuehling: you know, define those people with that field, or what, you know, however we want to do it. I don’t know the exact…

611 01:08:20.270 ⇒ 01:08:23.590 Judd Kuehling: Maybe it’s, like, what segment are they in, and then they’re in…

612 01:08:23.760 ⇒ 01:08:42.910 Judd Kuehling: you know, active, never active, you know, email, sign up, or whatever, like, however we want to do that, but I would love to have that, kind of, some of that stuff permanently in there, and it’s just constantly being updated, automatically, as opposed to… like, I don’t really want… ideally, I don’t want to do, kind of, like, one-off

613 01:08:43.210 ⇒ 01:08:44.149 Judd Kuehling: lists.

614 01:08:44.380 ⇒ 01:08:46.150 Judd Kuehling: In the customer I.O.

615 01:08:46.270 ⇒ 01:08:48.919 Judd Kuehling: There’s no need for that. If we can do a kind of…

616 01:08:49.210 ⇒ 01:08:52.870 Judd Kuehling: Consistent, constantly updated stuff, that’d be great.

617 01:08:52.870 ⇒ 01:08:53.420 Zoran Selinger: Yeah.

618 01:08:53.670 ⇒ 01:09:05.520 Zoran Selinger: Okay, okay, that makes sense. I’ll see if we can get… if we can get, basically, those four… four categories, which are active, which are inactive, or churned.

619 01:09:05.529 ⇒ 01:09:22.290 Zoran Selinger: Which are never active and recently inactive. So if you can get those four, hopefully you can… you can use them, especially for this, like, never active when it comes to treatments, it’s… it… it is really good, it’s a pre-qualified audience that…

620 01:09:22.340 ⇒ 01:09:26.420 Zoran Selinger: We should be able to get a good portion of them back.

621 01:09:28.779 ⇒ 01:09:34.460 Judd Kuehling: Oh, okay. So, most of the email campaigns are…

622 01:09:34.600 ⇒ 01:09:38.390 Judd Kuehling: keyed off of what CIO calls segments.

623 01:09:38.580 ⇒ 01:09:40.830 Judd Kuehling: And segments are built

624 01:09:41.270 ⇒ 01:09:49.580 Judd Kuehling: definitions of customers, and so, for example, we have, like, a segment called Churn 1 and Churn 2, so it’s…

625 01:09:49.590 ⇒ 01:09:52.459 Zoran Selinger: I saw them in the companions, yeah.

626 01:09:52.460 ⇒ 01:09:59.710 Judd Kuehling: And then we use the fields in Customer I.O. to define the segments. And so, for example, Churn 1 is, like.

627 01:10:00.070 ⇒ 01:10:03.959 Judd Kuehling: They’ve completed an order, they haven’t completed an order in 60 days.

628 01:10:04.140 ⇒ 01:10:10.470 Judd Kuehling: They’ve not completed more than two times, so it’s like one single order, and they haven’t done it in 60 days.

629 01:10:10.590 ⇒ 01:10:20.299 Judd Kuehling: And then Churn 2 is the same thing, but it’s more than one historical order. They haven’t completed in 60 days. But you can look at, kind of, the definitions within…

630 01:10:20.760 ⇒ 01:10:33.539 Judd Kuehling: segment… within those segments to see, kind of, how we’re using the fields in Customer I.O. to build segments, and then those segments are then used within email campaigns to get people and things like that.

631 01:10:34.770 ⇒ 01:10:49.159 Zoran Selinger: Okay, okay. I’ll talk to, so there’s, Ashwini on our side that we’re… I think he has a task about adding a new… new field into… into Customer I.O, so I’m gonna talk to him about…

632 01:10:49.240 ⇒ 01:10:55.559 Zoran Selinger: those four segments as… as well. Okay. If you can have… if you can end that field, so you can…

633 01:10:56.230 ⇒ 01:11:00.080 Zoran Selinger: you can work on campaigns for them. Okay, cool.

634 01:11:00.080 ⇒ 01:11:00.600 Greg Stoutenburg: Yep.

635 01:11:01.570 ⇒ 01:11:03.529 Zoran Selinger: That’s all for me, Greg. Anything else?

636 01:11:03.530 ⇒ 01:11:18.299 Greg Stoutenburg: Yep. Yep, no, that sounds good. Thanks for this, Zora and Judd. I think for next steps, Judd will wait on you for… you’re gonna reorganize the way you have your template for weekly presentations on your campaigns.

637 01:11:18.590 ⇒ 01:11:21.469 Judd Kuehling: Yeah, so I have this big Excel file that I have.

638 01:11:22.040 ⇒ 01:11:26.849 Judd Kuehling: data that I’m pulling in manually from Customer I.O,

639 01:11:27.230 ⇒ 01:11:43.240 Judd Kuehling: I’m using it mostly off of those tags that I told you about, so that would be another thing that you guys could look at, is those tags in Customer I.O. to define these groups, kind of see how that differs from what you were doing off of the campaign names itself.

640 01:11:43.490 ⇒ 01:11:48.810 Judd Kuehling: And then, I’ll send that over to you, kind of the…

641 01:11:49.360 ⇒ 01:12:07.469 Judd Kuehling: I need to do some changing to it, because we have kind of the… what I was able to do version right now, but kind of the more the ideal, what we’re looking for version that I need to kind of change it to, so… let me build that, and then I’ll send it over to you guys, and then we can kind of figure out how we can fill that in on a weekly basis.

642 01:12:07.800 ⇒ 01:12:24.529 Greg Stoutenburg: Yep, cool. Yeah, great. And then just… let’s just constantly be in touch about how we can be of most help for your campaign. My mind goes to, you know, some of the conceptual things about what the goals are per campaign, and making sure that we’re measuring the right thing based on what we want that customer experience to look like.

643 01:12:24.870 ⇒ 01:12:29.080 Greg Stoutenburg: Let’s just be in touch about it, and we’ll look forward to that outline, and go from there.

644 01:12:29.360 ⇒ 01:12:36.089 Judd Kuehling: Yeah, so, some of the challenge with a lot of this stuff is, the data that we can get out of BASC, so…

645 01:12:36.090 ⇒ 01:12:36.700 Greg Stoutenburg: Yeah.

646 01:12:36.700 ⇒ 01:12:47.040 Judd Kuehling: we can’t get as much data as we would love to out of BASC. There’s a ton of data there, and there’s a ton of, you know, stuff that happens where the customer goes through and…

647 01:12:47.550 ⇒ 01:13:05.529 Judd Kuehling: either does all the stuff in the intake, answers all these questions, and we get, like, just the basic stuff out of that. We don’t get, like, a lot of things out of that. If we could get more of that data eventually, that would be helpful. If we could get more data around what they’ve done in their portal, which is essentially their login into Basque, and kind of how they’ve…

648 01:13:06.120 ⇒ 01:13:23.879 Judd Kuehling: done different things there. They can, like, record their shots that they’ve given themselves, they can record… there’s all kinds of other things where they, like, choose to make different changes to their plan and everything like that. A lot of that data that we don’t get out of there, and so I know that’s not something that is going to be quick to get

649 01:13:23.970 ⇒ 01:13:30.960 Judd Kuehling: out of there, but as we kind of continue to add to the list of data we can get out of there, we can do a lot more, I think, too.

650 01:13:31.190 ⇒ 01:13:31.800 Greg Stoutenburg: Yeah.

651 01:13:31.990 ⇒ 01:13:32.590 Greg Stoutenburg: Yeah.

652 01:13:32.590 ⇒ 01:13:33.770 Judd Kuehling: That makes sense. Yeah.

653 01:13:33.770 ⇒ 01:13:44.260 Greg Stoutenburg: Yeah, yeah, agreed, yeah. Yeah, so we’ll align on what it is we want to find, and then figure out what those pieces of data are, get them sent in so you can use them in your campaigns.

654 01:13:44.260 ⇒ 01:13:44.800 Judd Kuehling: Yeah.

655 01:13:44.800 ⇒ 01:13:45.600 Greg Stoutenburg: Yup.

656 01:13:46.020 ⇒ 01:13:46.400 Zoran Selinger: Excellent.

657 01:13:46.740 ⇒ 01:13:47.570 Greg Stoutenburg: Great.

658 01:13:47.570 ⇒ 01:13:53.759 Zoran Selinger: Okay, Judd, thank you very much for waking up so early and joining.

659 01:13:53.760 ⇒ 01:13:59.049 Judd Kuehling: You guys, that was really helpful, and I think there’s a lot of other… much of other stuff we can do, so I’m excited.

660 01:13:59.050 ⇒ 01:13:59.390 Zoran Selinger: Yeah.

661 01:13:59.390 ⇒ 01:14:02.300 Greg Stoutenburg: Yep, of course. Yeah, same. Alright, sounds good. Thanks, guys. Thanks. See ya.

662 01:14:02.300 ⇒ 01:14:03.690 Zoran Selinger: Take care. Bye.

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